Cognitive Robotics最新文献

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Cognitive Robotics

Pub Date : 2026-01-01

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Cognitive Robotics

Pub Date : 2026-01-01

引用次数: 0

Underwater image super-resolution via multi-domain learning 基于多域学习的水下图像超分辨率

Cognitive Robotics

Pub Date : 2025-12-05 DOI: 10.1016/j.cogr.2025.11.002

Guanze Shen , Jingxuan Zhang , Zhe Chen

Underwater images suffer from haze effects and low contrast due to wavelength- and distance-dependent scattering and attenuation. These issues present significant challenges for various underwater vision applications. Super resolution (SR) of underwater images offers an effective solution for enhancing both detail refinement and overall image visibility. However, underwater image SR remains challenging owing to the severe degradation of texture and color information. This paper proposes a multidomain learning-based SR network to enhance the performance of underwater image SR. Specifically, we introduce a multidomain encoder network that integrates grayscale and dual-color spaces into a unified framework. This architecture enables our model to simultaneously improve the underwater image quality through texture enhancement and color correction. By incorporating a channel attention mechanism, the most discriminative features extracted from multiple domains can be adaptively weighted and fused. Consequently, our network effectively boosts image resolution and enhances visual quality by leveraging multidomain data and the advantages of learning-based approaches. Experimental results demonstrate the superior performance of the proposed model in underwater image SR.

由于波长和距离相关的散射和衰减，水下图像遭受雾霾效应和低对比度。这些问题对各种水下视觉应用提出了重大挑战。水下图像的超分辨率（SR）为增强细节细化和整体图像可见性提供了有效的解决方案。然而，由于纹理和颜色信息的严重退化，水下图像SR仍然具有挑战性。为了提高水下图像SR的性能，本文提出了一种基于多域学习的SR网络。具体而言，我们引入了一种将灰度空间和双色空间集成到统一框架中的多域编码器网络。这种结构使我们的模型能够通过纹理增强和色彩校正同时提高水下图像质量。通过引入通道注意机制，从多个领域中提取的最具区别性的特征可以自适应加权和融合。因此，我们的网络通过利用多域数据和基于学习的方法的优势，有效地提高了图像分辨率和视觉质量。实验结果表明，该模型在水下图像SR中具有良好的性能。

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引用次数: 0

Self-adaptive control of a two-point contact gripper for the precise handling of compliant objects in industrial robotics 工业机器人中精确处理柔顺物体的两点接触夹持器自适应控制

Cognitive Robotics

Pub Date : 2025-11-22 DOI: 10.1016/j.cogr.2025.11.001

Sarawit Cheewaratchanon , Jutamanee Auysakul , Paramin Neranon , Arisara Romyen

This paper presents a novel adaptive control framework for robotic grippers that handles a wide range of compliant objects by mimicking human grasping behaviour. The proposed system integrates three distinct control strategies: classical Proportional-Integral-Derivative (PID), Proportional-Integral-based Fuzzy Logic Control (PI-FLC), and Reinforcement Learning (RL) to achieve precise and safe force modulation during object manipulation. A two-finger gripper prototype was developed and experimentally validated using objects of varying stiffness levels, including rigid (iron, plastic) and deformable materials (silicone, foam, sponge). Real-time force control was benchmarked against human-defined reference profiles derived from tactile interaction experiments. The results demonstrate that while PID control provides satisfactory performance for rigid objects, it fails to adapt to nonlinear dynamics in soft materials. In contrast, the PI-Fuzzy and RL controllers can achieve superior force tracking, stability, and generalisation, closely aligning with human-like grasping patterns. The PI-Fuzzy controller excels in rule-based adaptability, while RL shows potential in learning optimal strategies across different compliance levels. This study underscores the significance of integrating classical and intelligent control strategies to improve robotic dexterity, safety, and autonomy, particularly in unstructured environments. The findings have meaningful implications for industrial automation, human-robot collaboration, and the effective manipulation of objects with varying stiffness.

本文提出了一种新的机器人抓手自适应控制框架，该框架通过模仿人类的抓取行为来处理各种柔性物体。该系统集成了三种不同的控制策略：经典的比例-积分-导数（PID）、基于比例-积分的模糊逻辑控制（PI-FLC）和强化学习（RL），以实现物体操作过程中精确和安全的力调制。研究人员开发了一个两指夹持器原型，并使用不同刚度水平的物体进行了实验验证，包括刚性（铁、塑料）和可变形材料（硅胶、泡沫、海绵）。实时力控制的基准是由触觉交互实验得出的人类定义的参考轮廓。结果表明，PID控制对刚性物体具有满意的控制效果，但对软质材料的非线性动力学特性不适应。相比之下，PI-Fuzzy和RL控制器可以实现卓越的力跟踪，稳定性和泛化，与人类的抓取模式紧密一致。pi -模糊控制器在基于规则的自适应方面表现出色，而强化学习在不同遵从性水平上表现出学习最优策略的潜力。该研究强调了集成经典和智能控制策略以提高机器人灵活性、安全性和自主性的重要性，特别是在非结构化环境中。研究结果对工业自动化、人机协作以及对不同刚度物体的有效操纵具有重要意义。

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Navigation control of unmanned aerial vehicles in dynamic collaborative indoor environment using probability fuzzy logic approach 基于概率模糊逻辑的动态协同室内环境下无人机导航控制

Cognitive Robotics

Pub Date : 2025-01-01 DOI: 10.1016/j.cogr.2025.02.002

Sameer Agrawal , Bhumeshwar K. Patle , Sudarshan Sanap

The development of drones in various applications makes it essential to address the critical issue of providing collision-free and optimal navigation in uncertain environments. The current research work aims to develop, simulate and experiment with the Probability Fuzzy Logic (PFL) controller for route planning and obstacle avoidance for drones in uncertain static and dynamic environments. The PFL system uses probability-based impact assessment and fuzzy logic rules to deal with unknowns and environmental changes. The fuzzy logic system takes in input about the distance of objects from the drone's front, left, and right sides, as well as the probability of collision based on the drone's speed and how close it is to the obstacles. The set of thirty fuzzy rules based on the distance of the obstacle from front left and right are defined to decide the output, i.e. speed of the drone and heading angle. The simulation environment is developed using MATLAB, with grid-based motion planning that accounts for static and dynamic obstacles. The system's performance is validated through simulations and real-world experiments, comparing path length and travel time. On comparing the simulation and experimental results, the proposed PFL-based controller has been proven to be efficient, accurate, and robust for both static and dynamic and simple to complex environments. The drones can plan the shortest and most collision-free path across all the scenarios, as depicted in the simulation and experimentation results. However, due to communication delay, inaccuracy of sensor response, environmental impact and motor delay, there are slight deviations between the simulation and experimentation values. Upon performing the error analysis, it is found that the error between the simulation and experimental value is within the range of 6.66 % in all the studied scenarios.

无人机在各种应用中的发展使得解决在不确定环境中提供无碰撞和最佳导航的关键问题至关重要。目前的研究工作旨在开发、仿真和实验概率模糊逻辑（PFL）控制器，用于无人机在不确定静态和动态环境下的路线规划和避障。PFL系统采用基于概率的影响评估和模糊逻辑规则来处理未知因素和环境变化。模糊逻辑系统从无人机的前部、左侧和右侧输入物体的距离，以及根据无人机的速度和距离障碍物的远近判断碰撞的概率。根据障碍物与前方左右的距离，定义30条模糊规则集来决定输出，即无人机的速度和航向角度。仿真环境采用MATLAB开发，基于网格的运动规划，考虑了静态和动态障碍物。通过仿真和实际实验验证了系统的性能，比较了路径长度和行程时间。通过仿真与实验结果的对比，证明了所提出的基于pfl的控制器无论在静态还是动态、从简单到复杂的环境中都具有高效、准确和鲁棒性。如仿真和实验结果所示，无人机可以在所有场景中规划最短和最无碰撞的路径。但由于通信延迟、传感器响应不准确、环境影响、电机延迟等因素，仿真值与实验值存在轻微偏差。通过误差分析发现，在所有的研究场景中，仿真值与实验值的误差都在6.66%的范围内。

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Cognitive Robotics

Pub Date : 2025-01-01

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Cognitive Robotics

Pub Date : 2025-01-01

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Cognitive Robotics

Pub Date : 2025-01-01

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TrOCR-driven seal instrument detection and recognition for cognitive robotic applications 用于认知机器人应用的trocr驱动密封仪器检测和识别

Cognitive Robotics

Pub Date : 2025-01-01 DOI: 10.1016/j.cogr.2025.10.001

Xuan Jin , Sheng Wang , Miaomiao Zhang , Guoteng Xu , Bingqi Hu , Hanlin Tang

Seal recognition, as a fundamental perception capability, is crucial for enabling cognitive robotic systems to autonomously interact with and understand physical documents in intelligent office and archival environments. While Transformer based optical character recognition (OCR) methods have recently achieved remarkable progress, the recognition of curved and degraded seal text remains a significant challenge. Traditional approaches often rely on cumbersome pipelines with limited robustness, which hampers their integration into robotic cognitive platforms. To address these issues, this paper proposes a novel perception framework that integrates the YOLO-based detection module with the TrOCR recognition model for seal content analysis. The framework enhances robotic perception through three core mechanisms: precise spatial localization, adaptive noise suppression, and efficient curved-text recognition. Experimental results demonstrate that the proposed approach achieves 94.8% accuracy in bent seal text recognition tasks, validating its effectiveness in complex, real-world scenarios. These findings highlight the potential of the method to serve as a reliable perception module within cognitive robotic systems for document understanding and autonomous decision-making.

印章识别作为一种基本的感知能力，对于认知机器人系统在智能办公和档案环境中自主地与物理文件交互和理解至关重要。虽然基于Transformer的光学字符识别（OCR）方法最近取得了显著进展，但弯曲和退化的密封文本的识别仍然是一个重大挑战。传统方法通常依赖于笨重的管道，鲁棒性有限，这阻碍了它们与机器人认知平台的集成。为了解决这些问题，本文提出了一种新的感知框架，该框架将基于yolo的检测模块与TrOCR识别模型集成在一起，用于印章内容分析。该框架通过三个核心机制增强机器人感知：精确的空间定位、自适应噪声抑制和高效的曲线文本识别。实验结果表明，该方法在弯曲印章文本识别任务中准确率达到94.8%，验证了其在复杂现实场景中的有效性。这些发现突出了该方法作为认知机器人系统中文档理解和自主决策的可靠感知模块的潜力。

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Cognitive Robotics

Pub Date : 2025-01-01

引用次数: 0

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